Autonomous camera-to-camera change detection system

ABSTRACT

Embodiments disclosed herein are directed to an autonomous camera-to-camera scene change detection system whereby a first camera controls a second camera without human input. More specifically, a first camera having a field of view may receive and process an image. Based on the processed image, the first camera sends instructions to a second camera to focus in on an area of interest or a target identified in the processed image.

CROSS-REFERENCE TO RELATED APPLICATION

This application is a continuation patent application of and claims thebenefit of U.S. Non-Provisional patent application Ser. No. 15/939,202,filed Mar. 28, 2018, and titled “AUTONOMOUS CAMERA-TO-CAMERA CHANGEDETECTION SYSTEM,” which is a continuation application of U.S.Non-Provisional patent application Ser. No. 14/840,248, filed Aug. 31,2015, now issued U.S. Pat. No. 9,984,466 and titled “AUTONOMOUSCAMERA-TO-CAMERA INTRUSION DETECTION SYSTEM,” which claims priority toU.S. Provisional Patent Application No. 62/044,824, filed Sep. 2, 2014,and titled “AUTONOMOUS CAMERA-TO-CAMERA INTRUSION DETECTION SYSTEM,” thedisclosures of each of which are hereby incorporated herein by referencein their entirety.

TECHNICAL FIELD

The present disclosure is directed to a scene change detection system.More specifically, the present disclosure is directed to a scene changedetection and object tracking system in which a first camera of thesystem is configured to control or otherwise send commands to a secondcamera of the system.

BACKGROUND

Security cameras and systems are used in many situations to help protectproperty and other assets. However, conventional security systemstypically require a security guard or even multiple security guards tospend many man hours watching live or recorded footage from thesecameras. After a few hours, the observers may get distracted, fatiguedor may simply miss activity that could be seen as potentially dangerous.In addition, a field of view of the cameras is typically locked or setbased on the position of the camera. Thus, a security guard may not beable to focus in on an area or person of interest. For scenes wherethere is a lot of activity, it may not be possible for observers todetect a change in the scene that may be a threat. Even if the threat isdetected, it may not be possible to track the threat. Additionally,detection systems are plagued with false alarms which can quickly becomevery annoying to operators.

It is with respect to these and other general considerations thatembodiments have been made. Although relatively specific problems havebeen discussed, it should be understood that the embodiments should notbe limited to solving the specific problems identified in thebackground.

SUMMARY

This summary is provided to introduce a selection of concepts in asimplified form that are further described below in the DetailedDescription section. This summary is not intended to identify keyfeatures or essential features of the claimed subject matter, nor is itintended to be used as an aid in determining the scope of the claimedsubject matter.

Embodiments disclosed herein are directed to an autonomouscamera-to-camera scene change detection system whereby a first cameracontrols a second camera without human input. More specifically, a firstcamera having a field of view may receive and process an image. Based onthe processed image, the first camera sends instructions to a secondcamera to focus in on an area or a target of interest identified in theprocessed image.

Also disclosed is an image sensor having a processing unit and a memorythat is coupled to the processing unit. The memory stores instructionsthat are executed by the processing unit to capture images. The imagesensor is configured to capture a first series of images and analyze thefirst series of images to determine one or more expected or anticipatedchanges in the first series of images. The image sensor may then capturea second series of images. Upon determining that at least one image inthe second series of images has one or more unanticipated, unexpected orunwanted changes, image capturing instructions are transmitted to asecond image sensor.

A method of capturing an image is also disclosed herein. This methodincludes receiving a reference image at a first image sensor andreceiving a second image at the first image sensor. Once the images arereceived, the second image is compared to the reference image todetermine one or more types of changes between the second image and thereference image. A filter may then be generated based on the determinedone or more types of changes between the second image and the referenceimage. A third image may then be captured by the first image sensor.Once the third image is captured, the filter is applied to the thirdimage to remove anticipated or expected changes from the third image.Once the expected changes have been removed, unexpected changes in thethird image may be identified.

BRIEF DESCRIPTION OF THE DRAWINGS

Embodiments of the present disclosure may be more readily described byreference to the accompanying drawings in which like numbers refer tolike items and in which:

FIG. 1 illustrates an example autonomous camera-to-camera scene changedetection system according to one or more embodiments of the presentdisclosure;

FIG. 2 illustrates a method for capturing and analyzing an imageaccording to one or more embodiments of the present disclosure;

FIG. 3 is a block diagram illustrating example physical components of acomputing device that may be used with one or more embodiments of thepresent disclosure;

FIG. 4 illustrates a method for setting up and calibrating an autonomouscamera-to-camera scene change detection system according to one or moreembodiments of the present disclosure;

FIG. 5 illustrates a method for processing images using various filtersaccording to one or more embodiments of the present disclosure;

FIG. 6 illustrates how various regions and zones in an image may bedefined for the synthesizer process analyses according to one or moreembodiments of the present disclosure;

FIG. 7 illustrates how a detection distance algorithm may be used todetect a size of a target according to one or more embodiments of thepresent disclosure;

FIG. 8 illustrates how a learn filter is developed according to one ormore embodiments of the present disclosure;

FIG. 9 illustrates how a blocking filter is applied to an imageaccording to one or more embodiments of the present disclosure;

FIG. 10 illustrates how a moveable camera may be calibrated according toone or more embodiments of the present disclosure;

FIG. 11 illustrates an example user interface for learning about,erasing or blocking portions of a received image according to one ormore embodiments of the present disclosure; and

FIG. 12 illustrates an example user interface that shows images from afirst type of camera and a second type of camera according to one ormore embodiments of the present disclosure.

DETAILED DESCRIPTION

Various embodiments are described more fully below with reference to theaccompanying drawings, which form a part hereof, and which show specificembodiments for practicing the embodiments described herein. However,various embodiments may be implemented in many different forms andshould not be construed as limited to the embodiments set forth herein;rather, these embodiments are provided so that this disclosure will bethorough and complete, and will fully convey the scope of the inventionto those skilled in the art. Embodiments may be practiced as methods,systems or devices. Accordingly, embodiments may take the form of ahardware implementation, an entirely software implementation or animplementation combining software and hardware aspects.

Embodiments disclosed herein are directed to an autonomouscamera-to-camera scene change detection system (hereinafter, “thesystem”). The system may include two or more cameras that autonomouslyoperate to automatically detect and track unwanted changes in a capturedscene, image or series of images. As used herein, the term “autonomous”means that human support or input is not required for the cameras, orfor the overall system, to operate. In some embodiments, the cameras areclosed-circuit television (CCTV) cameras although other cameras and/orimage sensors may be used.

As will be described in detail below, the system includes, among othercomponents, a synthesizer and a camera-to-camera communication protocol.The synthesizer filters an image captured by one of the cameras in orderto determine changes in the captured image (or series or sequences ofimages). In some implementations, the synthesizer may use a series offilters to remove known image or scene changes and/or remove changesthat may be created by changing environmental conditions. The remainingchanges are then assessed to determine whether the changes are apotential threat or if the changes will be ignored. If the changes aredetermined to be a potential threat, the first camera may thenautonomously command and/or control a second, moveable camera of thesystem to focus on, zoom-in on or otherwise track the changes that wereidentified. If the changes are not identified as a potential threat, thechanges are ignored.

For example, a first camera of the system may be fixed or otherwisemounted on a platform or structure. In this example, the first cameramay have a lens with a specific or fixed field of view. The first cameramay capture or otherwise receive an image from its image sensor. Theimage may then be processed by a synthesizer of the system. Once theimage has been processed, and based on the results of the processedimage, the first camera uses the camera-to-camera communication protocolto command or otherwise control a second, moveable camera. That is, thefirst camera may provide pointing instructions, zoom instructions and soon to the second camera which causes the second camera to focus on anarea or a target of interest that was identified in the processed image.

FIG. 1 illustrates an example autonomous camera-to-camera scene changedetection system 100 according to one or more embodiments of the presentdisclosure. More specifically, the scene change detection system 100 mayinclude a first type of camera or image sensor that is communicativelycoupled to second type of camera or image sensor. The first type ofcamera or image sensor may be stationary (e.g., fixed) having a fixedfield of view and the second camera may be moveable such that the secondcamera may be able to view any object or location within the field ofview of the first camera.

In other embodiments, the first type of camera or image sensor may bepartially stationary (e.g., have limited movement) and have a semi-fixedfield of view. In yet another embodiment, the first camera or imagesensor may be moveable and/or rotatable about various axes and provide avariety of different fields of view. In addition, various combinationsof the above are contemplated. It is also contemplated that the firstcamera or image sensor may be able to zoom in and out on a particulararea and so on.

Although the first type of camera or image sensor may be described asany of the above, the first camera or image sensor will be referred toherein as a fixed camera. As shown in FIG. 1, the scene change detectionsystem 100 may include a number of fixed cameras including fixed camera1 110, fixed camera 2 120 and fixed camera N 130.

In some embodiments, each fixed camera may be positioned or orientatedsuch that the fixed camera has a fixed or predetermined field of view.For example, fixed camera 1 110 may be pointed at a first set of XYcoordinates, fixed camera 2 120 may be pointed at a second set of XYcoordinates and fixed camera N 130 may be pointed at a third set of XYcoordinates. In some embodiments, each set of coordinates may be uniquewith respect to one another. In another embodiment, some of the XYcoordinates may overlap, or at least partially overlap, with each other.

Each of the fixed cameras in the scene change detection system 100 maybe communicatively coupled with a respective second camera or imagessensor. In some embodiments, the second camera or image sensor is amoveable camera or moveable image sensor. More specifically, the secondcamera or image sensor may be a pan tilt and zoom (PTZ) camera. AlthoughFIG. 1 and the remainder of the description of FIG. 1 refers to thesecond type of camera as a PTZ camera, embodiments of the presentdisclosure are not so limited. As such, the second camera or imagesensor may be any type of camera (e.g., a high definition sensor thatmay include various megapixel image sensors) for capturing live and/orstill images.

As shown in FIG. 1, each fixed camera in the system 100 may becommunicatively coupled with respective PTZ cameras. For example, fixedcamera 1 110 may be communicatively coupled to PTZ camera 1 111 andcommunicatively coupled to PTZ camera 2 121. Likewise, fixed camera 2120 may be communicatively coupled to PTZ camera 2 121 and PTZ camera N131. In addition, fixed camera N 130 may be communicatively coupled toPTZ camera N 131.

Although various configurations are shown and described, each of thecameras in the system 100 may be communicatively coupled to each of theother cameras in a variety of ways. For example, fixed camera 1 110 mayonly be coupled to PTZ camera 1 111 while fixed camera 2 120 may becommunicatively coupled to each PTZ camera in the system 100.

Further, the system 100 may include various combinations of thedifferent types of cameras. For example, the system 100 may contain onefixed camera and multiple PTZ cameras. Alternatively, the system 100 maycontain multiple fixed cameras and a single PTZ camera. In suchembodiments, or in the other embodiments described herein, each fixedcamera may have a priority with respect to the other fixed camera in thesystem 100.

The priority may be based on XY coordinates of the field of view or theposition of each fixed camera, an amount of unwanted change that isdetected in each received image (e.g., an image captured by the firstfixed camera has more unwanted changes than a second fixed camera andtherefor receives priority) and so on. When such a priority isestablished, the PTZ camera may be configured to communicate with thefixed camera having the first priority and then address the instructionsreceived from the fixed camera having the second priority.

In another embodiment, the priority between the fixed cameras may bepresent even if the fixed cameras are communicatively coupled tomultiple PTZ cameras. For example, fixed camera 1 110 and fixed camera 2120 may each be communicatively coupled to PTZ camera 1 111 and PTZcamera 2 121. If fixed camera 1 110 has a higher priority than fixedcamera 2 120, and both fixed cameras have detected unwanted changes in acaptured image, PTZ camera 1 111 may follow instructions received byfixed camera 1 110 and PTZ camera 2 121 may follow instructions receivedby fixed camera 2 120.

In some embodiments, when the PTZ cameras are coupled to multiple fixedcameras, the PTZ cameras may be configured to communicate to the fixedcameras that they are being utilized by a fixed camera with a higher (orlower) priority. As a result, the fixed camera may be instructed orotherwise configured to utilize another PTZ camera, interrupt thecurrent use of the PTZ camera by the fixed camera (e.g., a higherpriority fixed camera can interrupt use of a PTZ camera by a lowerpriority fixed camera) and/or wait until the PTZ camera is no longeractively receiving instructions from another fixed camera.

In certain embodiments, the fixed cameras may be communicatively coupledto the PTZ cameras using a variety of communication protocols includingcustom communication protocols (e.g., camera-to-camera communicationprotocols). For example, the fixed cameras may communicate with the PTZcameras using a network connection, an internet connection, wirelesscommunication protocols, analog signals and/or digital signals overhardwires, Supervisory Control and Data Acquisition (SCADA) and so on.

In addition, a first fixed camera of the system may use a first type ofcommunication protocol to communicate with one or more PTZ cameras whilea second type of camera in the system 100 may communicate with a PTZusing a second type of communication protocol. When a fixed camera iscommunicatively coupled to multiple PTZ cameras, the fixed camera maycommunicate with each PTZ camera using different protocols. In addition,if a PTZ camera is communicatively coupled to multiple fixed cameras,the PTZ camera may use various communication protocols in communicatingwith each fixed camera.

Each fixed camera may be communicatively coupled to the other fixedcameras in the system 100. For example, fixed camera 1 110 may becommunicatively coupled to fixed camera 2 120 and fixed camera N 130.Likewise, each PTZ camera in the system 100 may be communicativelycoupled to the other PTZ cameras in the system 100. For example, PTZcamera 1 111 may be communicatively coupled to PTZ camera 121 and PTZcamera N 131.

In such embodiments, one camera in the system, or one camera of eachtype in the system, may be the “master camera” that may control, or sendinstructions to, the other cameras in the system 100. As such, the“master camera” may have a field of view that encompasses, partially orfully, the fields of view of all the other cameras in the system 100.

Referring back to FIG. 1 and as discussed above, each fixed camera mayhave a set or predetermined field of view. In addition, each fixedcamera may be coupled to a PTZ camera that has the same or a similarfield of view. Thus, and as will be explained in more detail below, asthe fixed camera receives one or more images, it compares the receivedimages with one or more reference images and determines whether a scenechange or point of interest exists in the received images. If anunwanted or unexpected change exists, the fixed camera sendsinstructions to the PTZ camera to move or rotate toward and/or zoomtoward the point or target of interest that caused the unexpected orunwanted change.

Because the fixed camera is communicatively coupled to the PTZ camera inthe system 100 and may be configured to self-prioritize, the system 100may be configured to operate with little or no human interaction. Inaddition, the system 100 may be configured to self-prioritize. That is,each camera in the system 100, either working by itself or inconjunction with the other cameras in the system, can decide what tolook at, what changes pose potential threats and so on.

For example, fixed camera 1 110 may be pointed or otherwise orientatedat a first set of XY coordinates. As such, the fixed camera continuallyreceives a series of still images or a series of live images. As theimages are received by fixed camera 1 110, the images are processed andcompared against a reference image. As will be explained below, thereference image may be calibrated to account for natural and/or expectedmovement of various items within the XY coordinates.

For example, if the fixed camera 1 110 was capturing images or otherwiseorientated toward a grove of trees, it may be expected that wind maycause the trees to move. Likewise, the image may contain various cloudsin the sky, reflection off of buildings or bodies of water, glint,moving shadows due to the position of the sun and so on. As such, fixedcamera 1 110 is configured to analyze a fixed camera image by applying aseries of filters (derived during configuration of the fixed camera) tothe scene and applying the filters to each next resulting image. Morespecifically, any differences between the incoming image and thereference image are used to determine if any unanticipated or unwantedchanges occur within the scene.

When the unwanted change is determined, fixed camera 1 110 may instructPTZ camera 1 111 and/or PTZ camera 2 121, to move and/or zoom toward theunwanted or unanticipated change within the image. In some embodiments,the PTZ camera may move such that the unwanted change, or the targetcausing the unwanted change, is within a center or substantially thecenter of the images being captured.

In some embodiments, as the images are being captured by the fixed andPTZ cameras, the images may be sent to a viewer 140. The viewer may beremote with respect to the various cameras of the system 100. As such,the viewer 140 may be configured to stitch received images together. Theviewer 140 may be communicatively coupled to each camera in the systemor to individual cameras of the system using various communicationprotocols such as those described above. In addition, although a singleviewer 140 is shown, the system 100 may include multiple viewers 140.The viewer 140 may be used to enable an authorized operator or user toview, access and/or control the various camera and/or images in thesystem 100.

In some embodiments, each camera in the system 100 may be identified onthe network using, for example, a name, IP address or other suchidentifier. Using the identifier, each fixed camera may be associatedwith one or more PTZ cameras and vice versa.

In addition, each camera in the system may be configured to perform aself-calibration test. In some embodiments, the calibration of each PTZcamera is used to associate the motion of the PTZ camera with a field ofview of one or more fixed cameras the PTZ camera is associated with. Insome embodiments, the calibration of the PTZ camera may be manual,automatic or semi-automatic. More specifically, the calibration of thePTZ camera may be used to determine the orientation of the fixed cameraand pointing algorithms associated with the fixed camera's set of XYcoordinates. The pointing algorithms are used by the fixed camera toinvoke the camera-to-camera communication protocol and command the PTZcamera.

The calibration of the PTZ camera consists of using fixed pointcoordinates (e.g., Cartesian and angular coordinates) in the field ofview of the fixed camera. For example calibration includes centering aselected point in the fixed camera's field of view to a center of thefield of view of the PTZ camera. That is, the PTZ camera is instructed(by the fixed camera) to move until the filed of view of the PTZ camerais centered on the selected point. The selected point may be any pointwithin the field of the view of the fixed camera. This process may thenrepeat. More specifically, the fixed camera's field of view coordinatesare transmitted to the PTZ camera for its field of view coordinates. ThePTZ camera may then use these coordinates when it is commanded to moveby the fixed camera.

In another embodiment, the user may select specific objects in the fixedcamera image or field of view and select the same objects in themoveable camera. When the object has been selected, the processor unitassociated with the fixed camera updates pointing equations forautonomous control of the PTZ camera. Once this is established, the PTZcamera may become familiar with (e.g., know the bounds) the field ofview of the fixed camera. In other embodiments, the XY coordinates ofthe fixed camera field of view may be provided, either manually (e.g.,by an operator or user of the system 100) or automatically to the PTZcamera. In some implementations, these coordinates may be provided by apositioning device, such as, for example a GPS device.

In another embodiment, the PTZ camera may be calibrated based on one ormore inputs received by a user or operator of the system. In oneexample, a user or operator may select a target location in the fixedcamera image. The selection may be made using mouse clicks, input on atouch sensitive device, entering coordinates etc. Once the input hasbeen received, the moveable camera is aligned to the target location bythe operator. The location is then saved and additional points may beselected. This is shown and described in more detail with respect toFIG. 10.

In addition to the above, a user can point to a specified location inthe fixed camera window/image on a computer terminal or other inputdevice. In response, the PTZ camera will be directed to that locationvia the camera-to-camera communication protocol. Accordingly, while ajoystick, mouse, keyboard or other similar directional input mechanismmay be used, one is not required.

FIG. 2 illustrates a method for capturing and analyzing an imageaccording to one or more embodiments of the present disclosure. Themethod 200 may be used by the system 100 and/or one more cameras of thesystem 100 shown and described above with respect to FIG. 1.

Method 200 begins at operation 205 in which a reference image iscaptured by a first camera. In some embodiments, the first camera is afixed camera such as described above. The fixed camera then captures 210a second image. The second image is used to update the reference imagesuch as will be described below. Flow then proceeds to operation 215 andthe differences between the reference image and the second image aredetermined.

In some embodiments, the reference image and/or the second image may becaptured by an image sensor of the first camera. The image sensor may bea single sensor or an array of sensors. Each of the sensors may have aspecific field of view. Further, each field of view may be stitched toform a wider or broader field of view (e.g., 180 degrees or more). Inanother implementation, a camera sensor may be a high definition arraydivided into sections with specific fields of view (e.g., arrays withmillions of pixels) and so on.

Operation 215 uses a synthesizer to processes the reference image, thesecond image and any subsequent images. The synthesizer applies one ormore filters to the images, such as, for example, a curtain filter. Thesynthesizer may also adjust the sensitivity of the images. Using thefilters, the synthesizer is able to determine any differences, on aframe by frame basis, between the reference image and the second image.The differences may encompass all of the changes between the images fromthe field of view of the fixed camera.

Flow then proceeds to operation 220 and the changes are analyzed todetermine which, if any, of the changes are associated with changingenvironmental conditions. More specifically, the synthesizer consists ofa series of filters that analyze ambient conditions in the fixed camerafield of view. The filters enable the synthesizer to determine thepresence of clouds in the image, movement of shadows with respect tomovement of the sun and movement caused by weather conditions (e.g.,rain, snow, wind, hail, etc.). As the images are analyzed, the systemmay learn which changes in the scene can be ignored due to theenvironmental conditions and which may be potentially dangerous or posea threat.

Once the environmental changes have been determined, flow proceeds tooperation 225 and remaining changes (e.g., changes that are notenvironmental changes) are processed using another filter (referred toherein as a “known changes filter”). As its name implies, the knownchanges filter removes all known changes between the reference image andthe second image.

For example, a capture zone in the image may be identified. The capturezone may be an area of interest within the fixed camera field of view inwhich any changes are monitored. Put another way, a fixed camera fieldof view may be divided into different zones. Some zones may be monitoredfor changes while other zones are not. In some embodiments, the capturezone may be automatically determined. In other embodiments, the capturezone may be specified by a user or operator of the system. If changes inone of the zones are known or otherwise anticipated (e.g., differentcars driving on a highway), these known changes may be disregarded.

Flow then proceeds to operation 230 in which one or more noise filtersremove any noise in the image. The noise may consist of reflections,glint, camera platform motion caused by wind, etc.

In operation 235, the pixels of the image are grouped to form potentiala target. The size of each of the grouped pixels is analyzed (and may beadjusted) to detect the largest pixel group. The largest pixel groupthat is selected may be associated with a potential threat or anunwanted change.

In some embodiments, the pixel grouping process may also include settingand determining a minimum and/or a maximum number of pixels that may berequired to constitute an unwanted change. For example, if an object hasmoved into the field of view of the fixed camera but has a number ofgrouped pixels that exceed the maximum threshold (e.g., a semi-truckdriving down the road), the fixed camera may be configured to ignorethat the change in the image as it could be labeled as “anticipated.”Likewise, if the number of grouped pixels in the image is below athreshold (e.g., an animal moving into the field of view), that may alsobe ignored as an anticipated change.

Once the thresholds and the filters have been determined in the mannerdescribed above, these thresholds and filters are used when subsequentimages are captured and analyzed. For example and as discussed above, asynthesizer of the system may be configured to identify the anticipatedchanges within the fixed camera image that do not appear in thereference image. Thus, when another image is captured, the synthesizeris applied to the second image and the second image is subtracted fromthe reference image. The second image is then processed by thesynthesizer to remove all of the known or anticipated changes from theimage and/or ignore changes that do not match user or operatorestablished detection criteria. This leaves the unwanted change in thesecond image.

If unwanted changes are detected, flow proceeds to operation 240 and thesecond camera is instructed to pan, tilt and/or zoom toward the unwantedchange. For example, the first camera may send movement instructions tothe second camera and the second camera can capture and center theunwanted change within its field of view.

In some embodiments, the speed of movement of the second camera may beadjusted based on the position or distance the target is from the secondcamera. For example, as the target moves, the second camera may beconfigured to follow the movement of the target. In another embodiment,the position of the target in the field of view of the first cameraand/or the second camera may be used to determine movement of thepan/tilt platform and adjustment to the lens of the second camera.

Continuing the example, if the target image is in an upper portion ofthe field of view of the first camera, the target may be far away. Assuch, the second camera may need to zoom in on the target or otherwisemove such that the target is in the middle of (or close to the middleof) the field of view of the second camera. Likewise, if the target isnear the bottom of the field of view of the first camera and/or takes upa large portion of the field of view, the target may be closer to thefirst camera. As such, the second camera may need to zoom out orotherwise move such that the target is at or near the middle of thefield of view of the second camera.

The first camera may automatically adjust the settings (e.g., in pixels)of the second camera based on the determined distance of the target suchas discussed above. Thus, although the minimum and maximum size ofobjects (e.g., in pixels) may have been established such as describedabove, these values may be automatically and/or temporarily adjustedbased on the determined distance of the target. If the values aretemporarily adjusted, the synthesizer may determine if the change is anunwanted change or can be ignored.

FIG. 3 is a block diagram illustrating example components of a device300. The device 300 may be used in the camera-to-camera change detectionsystem described herein. In some embodiments, the components may be anintegrated system within a single structure such as a fixed camerahousing. While in other embodiments, the components may each be inseparate stand-alone units. Also, in some embodiments, the device 300may be a moveable camera such as described above with respect to FIG. 1.Although various components of the device 300 are shown, connections andcommunication channels between each of the components are omitted forsimplicity.

In a basic configuration, the device 300 may include at least oneprocessor 305 and an associated memory 310. The memory 310 may include,but is not limited to, volatile storage such as random access memory,non-volatile storage such as read-only memory, flash memory, or anycombination thereof. The memory 310 may store an operating system 315and one or more program modules 320 suitable for running software 355.The program modules 320 or software 355 may include modules and programsfor generating filters, performing synthesizer analysis (usingsynthesizer 360), controlling communications and/or movement of theother cameras and so on.

The device 300 may have additional features or functionality than thoseexpressly described herein. For example, the device 300 may also includeadditional data storage devices, removable and non-removable, such as,for example, magnetic disks, optical disks, or tape. Example storagedevices are illustrated in FIG. 3 by removable storage device 325 and anon-removable storage device 330.

In certain embodiments, various program modules and data files may bestored in the system memory 310. The program modules 320 and theprocessor 305 may perform processes such as, for example, one or moreoperations of the methods described herein.

As also shown in FIG. 3, the device 300 may include one or more sensors335. The sensors 335 may be any type of image sensor configured tocapture live and/or still images. Examples include single imagessensors, CCD sensor arrays and so on. The device 300 may also include,or be communicatively coupled with one or more output devices 340. Theoutput devices 340 may include a display and other such devices.

The device 300 also includes communication connections 345 thatfacilitate communications with additional devices 350. The additionaldevices may be other fixed or moveable camera processing units such asdescribed above with respect to FIG. 1. Such communication connections345 may include internet capabilities, direct connection capabilities, aRF transmitter, a receiver, and/or transceiver circuitry, universalserial bus (USB) communications, parallel ports and/or serial ports.

As used herein, the term computer-readable media may include computerstorage media. Computer storage media may include volatile andnonvolatile media and/or removable and non-removable media for thestorage of information. Examples include computer-readable instructions,data structures, and program modules. The memory 310, the removablestorage device 325, and the non-removable storage device 330 are allexamples of computer storage media. Computer storage media may includeRAM, ROM, electrically erasable read-only memory (EEPROM), flash memoryor other memory technology, CD-ROM, digital versatile disks (DVD) orother optical storage, magnetic cassettes, magnetic tape, magnetic diskstorage or other magnetic storage devices, or any other article ofmanufacture which can be used to store information and which can beaccessed by the device 300. Any such computer storage media may be partof the device 300.

FIG. 4 illustrates a method 400 for setting up and calibrating anautonomous camera-to-camera change detection system according to one ormore embodiments of the present disclosure. In some embodiments, themethod 400 may be used in conjunction with the autonomouscamera-to-camera change detection system such as described above withrespect to FIG. 1. Accordingly, reference may be made to a first type ofcamera (e.g., a fixed camera) and a second type of camera (e.g., amoveable camera) such as described above.

Method 400 begins at operation 410 in which a setup process isinitiated. The setup process may include a user or administratoraccessing an operating system of the first camera. In anotherembodiment, the user or administrator may access a computing system thatcontrols or otherwise communicates with the first camera and/or thesecond camera.

The setup phase may also include installing or otherwise positioningvarious cameras at certain locations. For example, a first camera may beinstalled at a first location and a second camera may be co-located withthe first camera.

In some embodiments, the second camera is located within a certaindistance (e.g., two feet) from the first camera. Because the firstcamera and the second camera are co-located, the second camera may beautomatically moved or otherwise controlled by the first camera to theabsolute position of any location within the field of view of the firstcamera. As such, the second camera may target an image and in the fieldof view of the first camera and zoom in on that image. In otherembodiments, the second camera may be any distance away from the firstcamera so long as the cameras can communicate with one another and solong as the second camera can pan, tilt or zoom toward an object ofinterest identified in the first camera's field of view (or in a fieldof view of a fixed camera that communicates with or is otherwiseassociated with the first camera).

The setup process may also include establishing a camera-to-cameracommunication protocol between the first camera and the second camera.In some embodiments, information about the cameras may be used toestablish the communication protocol. For example, the system maycollect a location addresses of each camera, manufacturer informationfor each camera, user names and passwords of users or administratorsthat access or otherwise control the cameras and so on.

In some implementations, the gathered information is used by thesynthesizer to apply the camera-to-camera protocol. More specifically,the synthesizer may use this information when the first camera providesmovement instructions to the second camera. In addition to the aboveinformation, the system may obtain IP addresses for each camera, videointerface communication information for each camera, radio broadbandinformation for each camera as well as any other communication protocolsand addresses that may be used to enable the cameras to communicate withone another.

After the initial setup, flow proceeds to operation 420 in whichdetection criteria for the first and/or second cameras are established.The detection criteria may include detection thresholds, filters,calibration information and the like.

For example and referring to FIG. 6, FIG. 6 illustrates an examplereference image 600 in which a user may provide defined threshold valuesfor specific detection requirements. For example, a user may specify thesmallest size 610 object (in pixels) that the synthesizer will identifywhen filtering an image. Changes smaller than this value will beignored. The user also specifies the largest size 620 object (in pixels)that the synthesizer should use when filtering the image. Changes largerthan this value will be ignored.

A field of view for the camera may also be specified. For example, auser may select or otherwise enclose an area 630 on the image 600 thatthe synthesizer will check for unwanted changes. Changes outside of thearea 630 may be ignored.

The setup detection criteria may also include a threshold that thesynthesizer will use in determining a size of the target. For exampleand as shown in FIG. 7, the user may define within an image 700 thevertical location threshold 710 for determining the size of a target.More specifically, a user may select a point in the image 700. Thevertical position of the point may be determined in pixels. This valueis a threshold value that is used by the synthesizer in a distancedetermination process. More specifically, the distance determinationprocess uses this value to assess the size of a target based on itsdistance from the first camera. The target's size may be adjusted basedon the vertical pixel position of the target relative to the line 710and its distance from the bottom of the image 700.

Referring back to FIG. 4, once the detection criteria have beenestablished, flow proceeds to operation 430 in which known changes arecaptured or otherwise determined. For example and referring to FIG. 8,the first camera may capture an image 800. To capture the image 800, auser may select a learn button 810. The captured image 800 is then usedas the reference image.

A second image is captured and the second image is subtracted from thereference image to create a difference image. Each pixel in thedifference image is checked for a value greater than a threshold value(e.g., 100). For any pixel value in the difference image that is greaterthan the threshold value, the reference image pixel is set to specifiedvalue (e.g., 255). This causes the reference image to show highlightedpixels 820.

The above process may be repeated a predetermined number of times oruntil the process is stopped by the user. The data derived during eachiteration of the process is added to the previous image so the endproduct (or the last image) is a sum of all data collected during theiterative process. When the process stops, the user has an image inwhich all changes in the scene are highlighted. The changes may becaused by any object's motion, reflections, glint from any crystal likeobject, i.e. concrete, snow, etc.

Once the learn process has stopped, the user may then select or “block”(shown by square 830) any area in the image 800 that should be ignoredby the synthesizer during the check for unwanted changes. For example,in some embodiments, the area outside the block 830 should be ignored.In another embodiment, the area inside the block 830 should be ignored.This image is then used as a motion filter. The blocked area (or areaswhen multiple blocks are formed) form a separate image that becomes thezone filter. Both filters are subsequently used by synthesizer. In someembodiments, the motion filter and the zone filter may be combined tocreate an “all” filter.

Referring back to FIG. 4, flow proceeds to operation 430 in which theblocked areas and known changes in an image are identified. For exampleand turning to FIG. 9, the pixels whose values are set to the predefinedvalue (e.g., 255) are identified and blocked out (e.g., with blacksquare 910). Thus, the camera and any other user may be prevented fromviewing this area.

More specifically, an operator or user may select any area on the image900 to highlight the area. The user may then erase one or more points inthe image 900 that should not be masked. All of the pixels in the areathat was blocked and erased returns to its original pixel value. Theimage is then saved as a curtain array. The curtain array issubsequently used by the synthesizer to know what areas to ignore whenchecking for unwanted changes. Thus, even if changes occur in the areacovered by the square 910, those changes will not be detected by thesystem.

Referring back to FIG. 4, flow then proceeds to operation 440 and thefirst camera and the second camera are calibrated. For example andreferring to the image 1000 of FIG. 10, the synthesizer may use the datapreviously collected to command the second camera to view unwantedchanges. That is, the synthesizer may use the data to determine whichareas in the image should be defined as targets for the second camera.

For example, the first camera may capture the image in the left window1010. A point in the left window 1010 is then identified and the firstcamera sends instructions to the second camera to pan, tilt and/or zoomto this location or near this location. The second camera captures asecond camera image and displays it in the window on the right 1020.

A user may also use motion buttons 1030 associated with the secondcamera to center the second camera on a selected point 1040 in thecenter of the second camera image 1020. After each point selected in thefirst camera image is centered in the second camera image, the point issaved.

Each time a point is saved, the processor unit saves the coordinates ofthe selected point in the first camera image and the pan, tilt and/orzoom values for the selected point of the second camera.

This process may be repeated a number of times for a number of differentareas or portions of the image 1000. Once this process is complete, theoptimum pole position and the optimum coefficients used to associate theselected points to the angular pan, tilt, and/or zoom motions of thesecond camera may be calculated. These values are used by thesynthesizer process to point the second camera to the unwanted change inthe first camera image. As used herein, the term ‘pole’ is the centerline axis of the moveable camera around which the camera moveshorizontally.

Once a user has performed all the setup requirements and the secondcamera has been calibrated such as described, flow proceeds to operation450 in which various operating modes of the cameras, or the system, maybe determined and/or selected. In some embodiments the different modesare selected by an operator of the system. In other embodiments, themodes may be automatically determined based on various parameters. Theseparameters may include, a location of the system or of one or morecameras in the system, the time of day, perceived threat level of theunwanted change and so on.

The different modes may consist of an armed mode, a passive monitor modeand an active monitor mode. Further the active monitor and armed modemay utilize the scene synthesizer and camera-to-camera protocol forcommand and control of the moveable camera such as will be described indetail below. The armed mode is configured to alert security personneland sends messages to first responders along with alert pictures. Thepassive monitor mode continuously captures and displays images from thefixed camera field of view only but may not send alerts to security orfirst responders. The active monitor mode continuously captures anddisplays images from the fixed and moveable cameras and uses the scenesynthesizer to automatically detect and track any change within thefixed camera's field of view but does not make video records or reportalarm events.

FIG. 5 illustrates a method 500 in which a synthesizer may analyzecaptured images according to one or more embodiments of the presentdisclosure. In some embodiments, the method 500 may be used inconjunction with the system 100 described above with respect to FIG. 1or with any of the methods described herein.

Method 500 begins at operation 505 when a reference image has beenupdated and/or a new image is obtained. In some embodiments, thereference image may be obtained by a first type of camera in a systemsuch as, for example, a fixed camera.

Flow then proceeds to operation 510 in which the synthesizer determineswhether a curtain filter is associated with the fixed camera 510. Forexample, as described above, a curtain filter may identify whichportions of an image are to be ignored. If a curtain filter is present,the synthesizer compares all points in the curtain filter with those inthe reference image and any secondary images. When each point in thevarious images match a point in the curtain filter, the reference andsecondary image points are identified. As a result, changes in theseareas will be ignored.

In operation 515 the images, and more particularly the synthesizersensitivity value, is adjusted based on fixed camera pixel noise andpixel changes in the image. These values are obtained by subtracting thenew image from the reference image. The synthesizer then creates ahistogram of the pixel values in the array and sequentially checks thenumber of pixels in each of the histogram groups. Each group is comparedwith maximum permitted sensitivity values to label and create a listingof the groups.

The synthesizer then begins to sequentially sum the number of pixels ineach group until the sum of the groups pixels are equal or greater thana threshold value (e.g., 30000). Once this threshold is reached, thesummation stops and the sensitivity is adjusted based on the totalnumber of groups that were included in the sum as well as one or morepredefined threshold constants.

The final value is compared to a table of predetermined values whereeach value in the table is associated with the smallest size groups ofpixels are detected. Following this check, the group count is comparedwith a table of maximum count versus target size values and the finalcount is adjusted to a limit defined by the table. This value becomesthe new sensitivity value. Using this data, the sensitivity may beautomatically raised or lowered.

Once the sensitivity has been adjusted, flow proceeds to operation 520in which the reference image is updated. In some embodiments, the datafrom the new images may be used to update the reference image. Morespecifically, the synthesizer may generate or create a new referenceimage by summing fractions of the actual pixel values from the referenceimage with fractions of the actual pixel values from the new image.

Each pixel in the updated reference is updated by the summation of onefourth (¼) of the pixel value in the current reference array with threefourths (¾) of the pixel value in the new image array. This may preventthe moveable camera from becoming fixed on the same target in a group oftargets and assures there will be more pixels on a target in frame toframe image captures. This weighted averaging permits the fixed camerato focus on the largest number of pixels target. Using this process, thefixed camera uses the moveable camera to automatically provide a view ofall targets in the group as new images are captured.

Operation 525 provides that changes due to changing environmentalconditions in the scene (e.g., the changes in the new image) aredetermined. In a more specific example, a determination may be madewhether moving clouds, rain or snow, or any other environmental factorsin the scene are contributing to changes within the new images.

A new array of the values derived from a pixel to pixel subtractionbetween the entire size of reference image and the entire size of thenew image is created. The synthesizer then counts the total number ofpixels in the difference array. If the number of pixels is greater thana predetermined threshold, there are too many environmental changes andthe sensitivity is set to a value that stops any further processing inthe synthesizer. The synthesizer repeats the previous operations untilthis value becomes less than the predetermined threshold.

When operation 525 completes, flow proceeds to operation 530 and adetermination is made as to the difference between the updated referenceimage and the new image. More specifically, the synthesizer subtractspixels in the new image from associated pixels in the reference image inorder to obtain a differences array between the new image and updatedreference image.

For example, filters (e.g., learned and blocked filters) of knownenvironmental changes and blocked areas in a scene that were createdduring setup of the fixed camera may be applied to the difference imageor array. More specifically, pixels in the difference array are set to azero (0) value everywhere a difference array pixel is in the samelocation as a learn or block filter pixel. That is, the synthesizer usesthe learned and blocked filters to remove known changes with the resultthat those changes do not trigger false alarms. As a result ofapplication of these filters, the system may be able to detect verysmall changes with minimal or no false alarms.

In operation 535, the value of each remaining pixel in the differencearray is compared to the sensitivity. More specifically, the remainingpixels form a new array where each pixel's value in the array iscompared with the sensitivity value to determine if the pixel has avalue that is greater than or equal to the sensitivity value that wasfound in operation 515. Pixel values that are below the threshold areconsidered noise and ignored. Pixel values that are greater than or ofequal value may form a new array and are processed further.

Flow then proceeds to operation 540 in which tests of the array areperformed to determine the proximity of any one pixel to any neighboringpixels in the array. In some embodiments, the pixels, and morespecifically pixels that are localized within a predetermined thresholddistance with respect to one another (e.g., 1,2,3 or 4 pixel locationsaway), are formed into groups. As described above, each group becomes apotential target. As a result of this process, only groups whose pixelsare above a certain sensitivity (e.g., 520) are the only groupsavailable for further processing.

In operation 545 the size value (e.g., the number of pixels) of any oneof the groups may be adjusted. For example, based on a group's verticalposition from a selected point in an image (e.g., a zeroth verticalraster line of a new image array and its position relative to theposition threshold), the group size value may be adjusted to larger orsmaller values. The size of each group is then compared to definedminimum and maximum threshold values. Any group whose values are notwithin the thresholds are ignored.

Flow then proceeds to operation 550 in which the shape of all the groupsare analyzed to determine if any group has been formed as a result ofany noise (e.g., glint, a wire, platform and or pole vibrations andother types of scene noise). The synthesizer selects a point in eachgroup and checks a ratio of the horizontal size/vertical size andvertical size/horizontal size. If the ratio is more than a predeterminedthreshold, the group is ignored.

In 555 the synthesizer uses determines the current pointing coordinatesof the fixed camera image with respect to a selected target. Once thecheck has been performed, flow proceeds to operation 560, in which apriority for target acquisition, pointing and viewing is established.

More specifically, the synthesizer may process all targets to check thesize of each target by comparing the each group's pixel size to allother groups. The largest group of pixels is deemed to be the target ofinterest. A command is then sent to the second camera to view orotherwise focus on the target of interest.

If other large pixel groups are also present, the system may cyclethrough each target group during repeated cycles. More specifically, ifone or more groups pass the size test, the fixed camera instructs themoveable camera to acquire the next target during each iteration of theprocess. It should be noted that because of the weighted averaging ofthe reference image with the new image, the largest target may not bethe largest target physically but the largest in terms of measured pixeldifferences.

In some embodiments, the synthesizer pointing algorithm includesdetermining a magnification required to maximize the size of the targetin the moveable camera field of view. The synthesizer is also configuredto calculate the amount of motion required to move to the new locationcoordinates. These values are also used to calculate the total amount oftime required for the moveable camera to execute the receivedadjustment.

The fixed camera uses the camera-to-camera protocol to transmit all ofthe above commands and information to the moveable camera and then waitsthe determined amount of time before capturing new images from the fixedand moveable cameras. The camera-to-camera commands includes commandsto: the target location coordinates; center the target in the PTZ cameralens field of view; adjust the moveable camera lens; magnify the fixedcamera selected target; and show a picture of the target after makingall adjustments.

Although described above, the following is a brief summary of the sampleimages shown in FIGS. 6-10 as well as other sample images shown in FIGS.11-12. In some embodiments, the sample images shown may include imagesand a user interface of the system described herein.

FIG. 6 illustrates how various regions and zones in an image may bedefined for the synthesizer process analyses according to one or moreembodiments of the present disclosure. For example, when an image isreceived such as shown in FIG. 6, various targets, target sizes, regionsand zones within the image may be defined.

The box 630 may be adjusted by clicking and dragging any of its corners.The area inside the box 630 defines the area the synthesizer will applyanalyses when checking the changes in the image 600. The additionalboxes can be adjusted in the same way and may be used to measurepotential target sizes whose size values may be input and saved in theboxes at the bottom of the window.

FIG. 7 illustrates how a detection distance algorithm may be used todetect a size of a target according to one or more embodiments of thepresent disclosure. For example, the detection distance algorithm may beused to determine one or more targets in an image based on, for example,the amount of pixels an image is comprised of and/or a position of thetarget in an image such as described above.

FIG. 8 illustrates how a learn filter is developed according to one ormore embodiments of the present disclosure. The highlighted areas arethe pixel points in the learn image array. The synthesizer will comparethese points with a difference image array and where there are locationmatches, that pixel in the differences array will be set to zero (0).

FIG. 9 illustrates how a blocking filter is applied to an image 900according to one or more embodiments of the present disclosure. Forexample, as shown in FIG. 9, the blocked out area 910 may be an area inwhich targets should be ignored.

FIG. 10 illustrates how a moveable camera may be calibrated according toone or more embodiments of the present disclosure. More specifically,FIG. 10 illustrates how a moveable camera may be calibrated with respectto a fixed camera such as described above. An operator begins byselecting a point in the left image 1010. The user may then use themotion buttons 1030 to move the moveable camera to center the pointselected in the left image 1010 in the moveable camera image on theright 1020. When the point is centered in the moveable camera image, amathematical algorithm calculates and saves coefficients associated withthe point 1040. The coefficients are then used to accurately point themoveable camera to the point 1040. Movement can occur whenever a pointaround the selected point 1040 is manually selected by an operator orthe coordinates for the point 1040 are determined by the synthesizerprocess for a camera-to-camera movement command.

FIG. 11 illustrates an example user interface 1100 for learning about,erasing or blocking portions of a received image 1110 according to oneor more embodiments of the present disclosure. For example, when theimage 1110 is received, one or more portions of the image 1110, or theentire image, may be blocked so the system can analyze or ignore variousmovement within the image or zone of the image such as described above.

FIG. 12 illustrates an example user interface 1200 that shows imagesfrom a first type of camera and a second type of camera according to oneor more embodiments of the present disclosure. As shown in FIG. 12, oncean image is received in a first camera, the first camera may directmovement and zoom of a second camera without, for example, humanintervention. The window at lower left is the first camera live picturewindow and is continuously updated to show real time first camerapictures. The window at upper right shows the pictures from the secondtype of camera that is commanded by the first camera. The upper leftwindow is a freeze window that always shows the point in the scene ofthe first camera where the second camera was commanded to point at andis commanded and controlled by the synthesizer process.

Embodiments of the present disclosure are described above with referenceto block diagrams and operational illustrations of methods and the like.The operations described may occur out of the order as shown in any ofthe figures. Additionally, one or more operations may be removed orexecuted substantially concurrently. For example, two blocks shown insuccession may be executed substantially concurrently. Additionally, theblocks may be executed in the reverse order.

It should be noted that the embodiments described herein may beapplicable to both products and services. As such, the terms may beinterchangeable with respect to one another. Thus, when a request or anoffer is made with respect to a product or item, the same or similaroffer or request may be made with respect to a service. As such, avendor may also be a service provider and a service provider may beviewed as a vendor. Further, a user of the system may be both a vendorand a consumer.

The description and illustration of one or more embodiments provided inthis disclosure are not intended to limit or restrict the scope of thepresent disclosure as claimed. The embodiments, examples, and detailsprovided in this disclosure are considered sufficient to conveypossession and enable others to make and use the best mode of theclaimed embodiments. Additionally, the claimed embodiments should not beconstrued as being limited to any embodiment, example, or detailprovided above. Regardless of whether shown and described in combinationor separately, the various features, including structural features andmethodological features, are intended to be selectively included oromitted to produce an embodiment with a particular set of features.Having been provided with the description and illustration of thepresent application, one skilled in the art may envision variations,modifications, and alternate embodiments falling within the spirit ofthe broader aspects of the embodiments described herein that do notdepart from the broader scope of the claimed embodiments.

1.-20. (canceled)
 21. A camera-to-camera control system, comprising: afirst camera having a fixed field of view, the first camera beingtrained to distinguish between an anticipated change in the fixed fieldof view and an unanticipated change in the fixed field of view; and asecond camera communicatively coupled to the first camera and having asecond field of view that is at least partially contained within, andmoveable within, the fixed field of view, the second camera adapted to:receive camera control instructions from the first camera in response tothe first camera detecting an unanticipated change caused by an objectof interest in the fixed field of view, the camera control instructionscomprising one or more of: instructions for tilting the second camera;instructions for panning the second camera; instructions for zooming thesecond camera; or instructions for tracking movement of the object ofinterest as the object of interest moves through the fixed field ofview.
 22. The camera-to-camera control system of claim 21, wherein thecamera control instructions further comprise instructions for capturingone or more images of the object of interest as the object of interestmoves through the fixed field of view.
 23. The camera-to-camera controlsystem of claim 21, wherein the fixed field of view is divided into atleast a first zone and a second zone.
 24. The camera-to-camera controlsystem of claim 23, wherein a size of at least one of the first zone orthe second zone is automatically determined.
 25. The camera-to-cameracontrol system of claim 23, wherein a size of at least one of the firstzone or the second zone is based, at least in part, on received input.26. The camera-to-camera control system of claim 21, wherein the cameracontrol instructions comprise movement speed instructions for the secondcamera.
 27. The camera-to-camera control system of claim 21, wherein theanticipated change is a result of an environmental condition.
 28. Thecamera-to-camera control system of claim 21, wherein the anticipatedchange is a result of a current time of a day.
 29. A method, comprising:capturing a first set of one or more images of an area of interest usinga first camera having a first field of view; training the first camera,using the first set of one or more images, to ignore anticipated changesthat occur within the area of interest; capturing a second set of one ormore images of the area of interest; detecting based, at least in part,on the second set of one or more images, an unanticipated change withinthe area of interest; in response to detecting the unanticipated changewithin the area of interest, causing the first camera to send cameracontrol instructions to a second camera having a second field of viewwithin the area of interest, the camera control instructions comprisingat least one of: pan, tilt and/or zoom instructions that cause thesecond camera to alter the second field of view within the area ofinterest; or instructions to capture one or more images of the area ofinterest.
 30. The method of claim 29, wherein the camera controlinstructions further comprise instructions for capturing one or moreimages of an object of interest within the area of interest.
 31. Themethod of claim 30, wherein the camera control instructions furthercomprise instructions for tracking movement of the object of interest.32. The method of claim 29, further comprising causing the first camerato send the camera control instructions to a third camera.
 33. Themethod of claim 32, wherein the second camera is associated with a firstpriority and the third camera is associated with a second priority. 34.The method of claim 29, wherein the anticipated changes are a result ofan environmental condition.
 35. The method of claim 29, wherein theanticipated changes are a result of a current time of a day.
 36. Amethod, comprising: training a first camera of a camera-to-cameracontrol system to ignore anticipated changes within a fixed field ofview of the first camera; upon completion of the training, detecting anobject of interest in the fixed field of view; and causing the firstcamera to send camera control instructions to a second camera, thesecond camera having a second field of view that is at least partiallywithin the first field of view, the camera control instructionscomprising instructions for causing the second camera to track movementof the object of interest.
 37. The method of claim 36, furthercomprising causing the second camera to capture one or more images ofthe object of interest as the object of interest moves.
 38. The methodof claim 36, wherein the training comprises applying a filter to one ormore captured images.
 39. The method of claim 36, wherein the cameracontrol instructions include movement speed instructions for the secondcamera as the second camera tracks the movement of the object ofinterest.
 40. The method of claim 36, wherein the anticipated changesare changes in an environment within the fixed field of view.